The pattern recognition technique of optical power spectral analysis (OPS) is well-known. In accordance with this technique, a lens is utilized to take the Fourier transform of samples of a pattern, and the resulting Fraunhoffer diffraction patterns are characterized, such as by the use of a wedge-ring detector and digital analysis to classify the pattern. While OPS has demonstrated significant advantages for rapidly classifying simple patterns, it has been found that as the pattern sets become larger and more complicated, the required software inevitably becomes more complex, the equipment requirements more demanding, and the processing time increases. Further, potential ambiguity of the data due to the loss of phase information places an upper limit on the information content, thus limiting the complexity of the patterns which can be classified by this technique.
The recognition technique of digital image processing is also well-known in the art. In accordance with this technique, an image which is sampled in the space domain is digitized and is loaded into a computer and processed in accordnce with known pattern recognition algorithms which can involve electronically taking the Fourier transform function. While digital image processing techniques are capable of high sample resolution and have the advantage of being able to recognize complex patterns of high space-bandwidth product, due to the great amount of information which must be processed, such techniques are undesirably slow. For example, in classifying the high space-bandwidth product patterns present in aerial photographic transparencies, wherein a frame may typically include 625 sampling locations, each of which may be resolved into 1024 sub-areas for digital processing, it has been found that even the fastest digital computers may not meet acceptable time constraints.